Shape threat detection via adaptive computed tomography

Ahmad Masoudi; Ratchaneekorn Thamvichai; Mark A. Neifeld

doi:10.1117/12.2223348

12 May 2016 Shape threat detection via adaptive computed tomography

Ahmad Masoudi, Ratchaneekorn Thamvichai, Mark A. Neifeld

Proceedings Volume 9847, Anomaly Detection and Imaging with X-Rays (ADIX); 98470H (2016) https://doi.org/10.1117/12.2223348
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

X-ray Computed Tomography (CT) is used widely for screening purposes. Conventional x-ray threat detection systems employ image reconstruction and segmentation algorithms prior to making threat/no-threat decisions. We ﬁnd that in many cases these pre-processing steps can degrade detection performance. Therefore in this work we will investigate methods that operate directly on the CT measurements. We analyze a ﬁxed-gantry system containing 25 x-ray sources and 2200 photon counting detectors. We present a new method for improving threat detection performance. This new method is a so-called greedy adaptive algorithm which at each time step uses information from previous measurements to design the next measurement. We utilize sequential hypothesis testing (SHT) in order to derive both the optimal "next measurement" and the stopping criterion to insure a target probability of error P_e. We ﬁnd that selecting the next x-ray source according to such a greedy adaptive algorithm, we can reduce P_e by a factor of 42.4× relative to the conventional measurement sequence employing all 25 sources in sequence.

Citation Download Citation

Ahmad Masoudi, Ratchaneekorn Thamvichai, and Mark A. Neifeld "Shape threat detection via adaptive computed tomography", Proc. SPIE 9847, Anomaly Detection and Imaging with X-Rays (ADIX), 98470H (12 May 2016); https://doi.org/10.1117/12.2223348

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